Circle Extraction Research Articles

Attitude measurement of ultraclose-range spacecraft based on improved YOLOv5s and adaptive Hough circle extraction.

In order to fulfill the requirements for various operations in space, such as rendezvous, docking, and capturing, there is a pressing need to achieve ultraclose-range spacecraft pose measurement. This paper addresses the challenges of pose measurement under low-light conditions at ultraclose range by introducing a stereovision solution based on target detection and adaptive circle extraction. Initially, an improved target detection algorithm is employed to expedite feature object detection. Subsequently, an adaptive circle extraction algorithm is developed through analysis of camera imaging to surmount challenges related to feature extraction and potential feature loss in the space environment. This approach facilitates swift and accurate measurement of spacecraft at ultraclose range. The results showcase a 66.36% reduction in parameter count for the enhanced target detection algorithm compared with the prevalent YOLOv7_tiny algorithm. Additionally, the adaptive circle extraction algorithm demonstrates an 11.4% increase in cooperative target feature extraction precision compared with existing methods while maintaining requisite detection speed. Simulation experiments indicate that the real-time position measurement error for spacecraft at ultraclose range is less than 0.18mm, and angle measurement error is less than 0.05°. This presents a viable visual solution for spacecraft pose measurement at ultraclose range in low-light environments.

AGConv: Adaptive Graph Convolution on 3D Point Clouds.

Convolution on 3D point clouds is widely researched yet far from perfect in geometric deep learning. The traditional wisdom of convolution characterises feature correspondences indistinguishably among 3D points, arising an intrinsic limitation of poor distinctive feature learning. In this article, we propose Adaptive Graph Convolution (AGConv) for wide applications of point cloud analysis. AGConv generates adaptive kernels for points according to their dynamically learned features. Compared with the solution of using fixed/isotropic kernels, AGConv improves the flexibility of point cloud convolutions, effectively and precisely capturing the diverse relations between points from different semantic parts. Unlike the popular attentional weight schemes, AGConv implements the adaptiveness inside the convolution operation instead of simply assigning different weights to the neighboring points. Extensive evaluations clearly show that our method outperforms state-of-the-arts of point cloud classification and segmentation on various benchmark datasets. Meanwhile, AGConv can flexibly serve more point cloud analysis approaches to boost their performance. To validate its flexibility and effectiveness, we explore AGConv-based paradigms of completion, denoising, upsampling, registration and circle extraction, which are comparable or even superior to their competitors.

A sub-pixel circle detection algorithm combined with improved RHT and fitting

Circle extraction is usually a pre-completed task used in different applications related to medical, robotics, biometrics image analysis among others. Randomized Hough Transform (RHT) determines the parameters of the circle by randomly obtaining three edge pixels, if they are not precisely located on the circumference. The detected circle will not perfectly match the ideal circle. At the same time, three random points are largely not on a circle, which leads to some invalid sampling and parameter accumulation. In this paper, an improved RHT combined with fitting subpixel circle detection algorithm is proposed. The improved RHT algorithm calculates and accumulates parameters by using 1 point obtained from random sampling and another two points obtained from horizontal and vertical search respectively. The algorithm introduces the edge map of the de-soliton point and small region, and improves the probability that three points belong to the same circle. Then, the set of edge pixels corresponding to the identified circle is fitted to reduce the bias effect caused by only using three edge pixels to calculate the circle parameters. In this way, the reliability of the fitting and the precision of the parameters are improved while removing the noise. Experimental tests were conducted for detection performance, accuracy of parameter estimation and noise robustness. Compared with other methods, the proposed method has strong anti-interference ability and high calculation accuracy.

Topological Charge Detection Using Generalized Contour-Sum Method from Distorted Donut-Shaped Optical Vortex Beams: Experimental Comparison of Closed Path Determination Methods

A generalized contour-sum method has been proposed to measure the topological charge (TC) of an optical vortex (OV) beam using a Shack–Hartmann wavefront sensor (SH-WFS). Moreover, a recent study extended it to be workable for measuring an aberrated OV beam. However, when the OV beam suffers from severe distortion, the closed path for circulation calculation becomes crucial. In this paper, we evaluate the performance of five closed path determination methods, including watershed transformation, maximum average-intensity circle extraction, a combination of watershed transformation and maximum average-intensity circle extraction, and perfectly round circles assignation. In the experiments, we used a phase-only spatial light modulator to generate OV beams and aberrations, while an SH-WFS was used to measure the intensity profile and phase slopes. The results show that when determining the TC values of distorted donut-shaped OV beams, the watershed-transformed maximum average-intensity circle method performed the best, and the maximum average-intensity circle method and the watershed transformation method came second and third, while the worst was the perfect circles assignation method. The discussions that explain our experimental results are also given.

Automatic circle detection on images using the Teaching Learning Based Optimization algorithm and gradient analysis

Circle extraction is usually a previous task used in different applications related to biometrics, robotics, medical image analysis among others. Solutions based on meta-heuristic approaches, such as evolutionary and swarm-based algorithms, have been adopted in order to overcome the main deficiencies of Hough Transform methods. In this paper, the task of circle detection is presented as an optimization problem, where each circle represents an optimum within the feasible search space. To this end, a circle detection method is proposed based on the Teaching Learning Based Optimization algorithm, which is a population-based technique that is inspired by the teaching and learning processes. Additionally, improvements to the evolutionary approach for circle detection are obtained by exploiting gradient information for the construction of the search space and the definition of the objective function. To validate the efficacy of the proposed circle detector, several tests using noisy and complex images as input were carried out, and the results compared with different approaches for circle detection.

Automatic multi‐circle detection on images using the teaching learning based optimisation algorithm

Circle detection has numerous applications towards industry, robotics, and science in general. Therefore, a significant effort has been made in order to develop an accurate and fast method for circle extraction. Commonly, different techniques such as the ones based on the Hough transform have been widely used because of their robustness. However, these techniques usually demand a considerable computational load and large storage, and therefore meta-heuristic approaches such as evolutionary and swarm-based algorithms have been studied as an alternative. This study introduces a circle-detection method based on a recently proposed meta-heuristic technique: the teaching learning based optimisation algorithm, which is a population-based technique that is inspired by the teaching and learning processes. The algorithm uses the encoding of three points as candidate circles over the edge image. To evaluate if such candidate circles are actually present within the edge map, an objective function is used to guide the search. To validate the efficacy of the proposed approach, several tests using noisy and complex images as input were carried out, and the results were compared with different approaches for circle detection.

Efficient Sleeping Beauty DNA Transposition From DNA Minicircles

DNA transposon-based vectors have emerged as new potential delivery tools in therapeutic gene transfer. Such vectors are now showing promise in hematopoietic stem cells and primary human T cells, and clinical trials with transposon-engineered cells are on the way. However, the use of plasmid DNA as a carrier of the vector raises safety concerns due to the undesirable administration of bacterial sequences. To optimize vectors based on the Sleeping Beauty (SB) DNA transposon for clinical use, we examine here SB transposition from DNA minicircles (MCs) devoid of the bacterial plasmid backbone. Potent DNA transposition, directed by the hyperactive SB100X transposase, is demonstrated from MC donors, and the stable transfection rate is significantly enhanced by expressing the SB100X transposase from MCs. The stable transfection rate is inversely related to the size of circular donor, suggesting that a MC-based SB transposition system benefits primarily from an increased cellular uptake and/or enhanced expression which can be observed with DNA MCs. DNA transposon and transposase MCs are easily produced, are favorable in size, do not carry irrelevant DNA, and are robust substrates for DNA transposition. In accordance, DNA MCs should become a standard source of DNA transposons not only in therapeutic settings but also in the daily use of the SB system.

The Extraction of Circles from Arcs Represented by Extended Digital Lines

This paper presents a new fast and robust circle extraction method that is capable of extracting circles from images with complicated backgrounds. It is not based on the Hough transform (HT) that requires a time-consuming voting process. The proposed method uses a least-squares circle fitting algorithm for extracting circles. The arcs are fitted by extended digital lines that are extracted by a fast line extraction method. The proposed method calculates accurate circle parameters using the fitted arcs instead of evidence histograms in the parameter space. Tests performed on various real-world images show that the proposed method quickly and accurately extracts circles from complicated and heavily corrupted images.

Efficiency of Hollow Fiber Modules for Nondispersive Chemical Extraction

In this work the efficiency of hollow fiber solvent extraction for the system Meaq/bis(2-ethylhexyl) phosphate (DEPA) in isododecane (Me = Cd, Ni, Zn) has been studied from two points of view: wastewater treatment and hydrometallurgy. It was possible to achieve 2−4 orders of magnitude decrease of metal concentration in the aqueous phase by extraction in a single-pass flow mode. Furthermore the attainable phase ratios permit very high enrichment of metallic species (ca. 3 orders of magnitude) in the circle extraction−reextraction. Previously performed thorough studies on the kinetics and mechanism of the mass transfer in the investigated system (interfacial reaction, transport-limited process) allowed predictive modeling of hollow fiber reactive extraction. The necessary values of mass transfer coefficient were calculated using correlations existing in the literature, which were checked experimentally. The obtained results of extraction were compared with those for a pulsed sieve-plate column and an ideal mixer−settler cascade. It was shown that one module (54 cm length, 9000 fibers, or 25 cm and 31 000 fibers) can replace an extraction column of 6 m length and two to four ideal stages. Presented results indicate the use of hollow fiber modules as high performance extraction devices.

Detecting the wheel pattern of a vehicle using stereo images

This paper presents a method for detecting the wheels of a vehicle in stereo image pairs. The method consists of two steps: (i) geometrical transformation; and (ii) circle extraction. The geometrical transformation uses the disparity values obtained from a stereo image pair to calculate the parameters of the plane containing wheels of the vehicle. By using these parameters, we transform any elliptical wheels contained in the plane to circular ones which can be extracted by the circle extraction algorithm. The circle extraction algorithm consists of (1) template matching and (2) Hough transform. In order to save computation and improve the results in the Hough transform, we employ two constraints (a) the neighbor-region edge connectivity and (b) the gradient direction of each edge point, to eliminate non-circular edge points. Experimental results show that these two constraints do eliminate non-circular edge points and preserve any circle embedded in edges. From the final results, we can observe that our method can detect and locate the wheels of a vehicle successfully.

An Algorithm for Circle Extraction and its Application to Superimposed Object Recognition

An Algorithm for Circle Extraction and its Application to Superimposed Object Recognition